class_docstr: Defines a configuration class from which a R2D2 Algorithm can be built.
  
  Example:
      >>> from rllib_r2d2.r2d2.r2d2 import R2D2Config
      >>> config = R2D2Config()
      >>> print(config.h_function_epsilon)  # doctest: +SKIP
      >>> replay_config = config.replay_buffer_config.update(
      >>>     {
      >>>         "capacity": 1000000,
      >>>         "replay_burn_in": 20,
      >>>     }
      >>> )
      >>> config.training(replay_buffer_config=replay_config)\  # doctest: +SKIP
      >>>       .resources(num_gpus=1)\
      >>>       .rollouts(num_rollout_workers=30)\
      >>>       .environment("CartPole-v1")
      >>> algo = R2D2(config=config)  # doctest: +SKIP
      >>> algo.train()  # doctest: +SKIP
  
  Example:
      >>> from rllib_r2d2.r2d2.r2d2 import R2D2Config
      >>> from ray import air
      >>> from ray import tune
      >>> config = R2D2Config()
      >>> config.training(train_batch_size=tune.grid_search([256, 64])
      >>> config.environment(env="CartPole-v1")
      >>> tune.Tuner(  # doctest: +SKIP
      ...     "R2D2",
      ...     run_config=air.RunConfig(stop={"episode_reward_mean":200}),
      ...     param_space=config.to_dict()
      ... ).fit()
  
  Example:
      >>> from rllib_r2d2.r2d2.r2d2 import R2D2Config
      >>> config = R2D2Config()
      >>> print(config.exploration_config)  # doctest: +SKIP
      >>> explore_config = config.exploration_config.update(
      >>>     {
      >>>         "initial_epsilon": 1.0,
      >>>         "final_epsilon": 0.1,
      >>>         "epsilone_timesteps": 200000,
      >>>     }
      >>> )
      >>> config.training(lr_schedule=[[1, 1e-3, [500, 5e-3]])\
      >>>       .exploration(exploration_config=explore_config)
  
  Example:
      >>> from rllib_r2d2.r2d2.r2d2 import R2D2Config
      >>> config = R2D2Config()
      >>> print(config.exploration_config)  # doctest: +SKIP
      >>> explore_config = config.exploration_config.update(
      >>>     {
      >>>         "type": "SoftQ",
      >>>         "temperature": [1.0],
      >>>     }
      >>> )
      >>> config.training(lr_schedule=[[1, 1e-3, [500, 5e-3]])\
      >>>       .exploration(exploration_config=explore_config)
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docstr: Initializes a ApexConfig instance.
docstr_tok: ["Initializes","a","ApexConfig","instance","."]
file_path: ray/rllib_contrib/r2d2/src/rllib_r2d2/r2d2/r2d2.py
imports: import logging
  from typing import Optional, Type
  from rllib_r2d2.r2d2.r2d2_tf_policy import R2D2TFPolicy
  from rllib_r2d2.r2d2.r2d2_torch_policy import R2D2TorchPolicy
  from ray.rllib.algorithms.algorithm_config import AlgorithmConfig, NotProvided
  from ray.rllib.algorithms.dqn import DQN, DQNConfig
  from ray.rllib.policy.policy import Policy
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import DEPRECATED_VALUE
label_desc: Use image node_id 1 to create a new R2D2Config object from inherited base classes: DQNConfig with example: obj = R2D2Config(algo_class)